Welcome to the eSports Match Outcome Prediction Project! This project focuses on developing a machine learning model to predict match outcomes in eSports, specifically targeting the game Valorant. By leveraging historical data on team performance, map win rates, and previous match results, this model aims to provide accurate predictions on which team will win a match. Table of Contents
The project consists of a machine learning model trained using logistic regression to predict match outcomes. The model incorporates various features such as team performance metrics, map win rates, and historical match results to make accurate predictions. Additionally, a user-friendly interface is provided to input team names and obtain predictions on match outcomes. Features
• Prediction of match outcomes in Valorant eSports matches.
• Integration of machine learning model into a production environment.
• Rigorous testing and validation procedures to ensure model reliability.
• Python
• Pandas
• Scikit-learn
• Logistic Regression
• Data Visualization (Matplotlib, Seaborn)
To get started with the project, follow these steps:
- Clone the repository to your local machine.
- Install the required dependencies using pip install -r requirements.txt.
- Run the main script to train the machine learning model and deploy it.
- Access the user interface to input team names and obtain match predictions. Usage To use the project, simply follow the instructions outlined in the "Getting Started" section above. Once the model is deployed, you can interact with the user interface to input team names and receive predictions on match outcomes. Contributing Contributions to the project are welcome! If you would like to contribute, please follow these steps:
- Fork the repository.
- Create a new branch (git checkout -b feature/add-new-feature).
- Make your changes.
- Commit your changes (git commit -am 'Add new feature').
- Push to the branch (git push origin feature/add-new-feature).
- Create a new Pull Request.